{"id":"https://openalex.org/W4211012142","doi":"https://doi.org/10.1109/ccnc49033.2022.9700511","title":"Data Importance Aware Periodic Machine Learning Model Update for Sparse Mobile Crowdsensing","display_name":"Data Importance Aware Periodic Machine Learning Model Update for Sparse Mobile Crowdsensing","publication_year":2022,"publication_date":"2022-01-08","ids":{"openalex":"https://openalex.org/W4211012142","doi":"https://doi.org/10.1109/ccnc49033.2022.9700511"},"language":"en","primary_location":{"id":"doi:10.1109/ccnc49033.2022.9700511","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ccnc49033.2022.9700511","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE 19th Annual Consumer Communications & Networking Conference (CCNC)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5009537247","display_name":"Yuichi Inagaki","orcid":"https://orcid.org/0000-0003-4157-5057"},"institutions":[{"id":"https://openalex.org/I22299242","display_name":"Kyoto University","ror":"https://ror.org/02kpeqv85","country_code":"JP","type":"education","lineage":["https://openalex.org/I22299242"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Yuichi Inagaki","raw_affiliation_strings":["Kyoto University Yoshida-Honmachi,Graduate School of Informatics,Sakyo-ku, Kyoto,Japan,606-8501"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Kyoto University Yoshida-Honmachi,Graduate School of Informatics,Sakyo-ku, Kyoto,Japan,606-8501","institution_ids":["https://openalex.org/I22299242"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087831856","display_name":"Ryoichi Shinkuma","orcid":"https://orcid.org/0000-0003-2842-8941"},"institutions":[{"id":"https://openalex.org/I171481255","display_name":"Shibaura Institute of Technology","ror":"https://ror.org/020wjcq07","country_code":"JP","type":"education","lineage":["https://openalex.org/I171481255"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Ryoichi Shinkuma","raw_affiliation_strings":["Shibaura Institute of Technology,Faculty of Engineering,Tokyo,Japan,135-8548"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shibaura Institute of Technology,Faculty of Engineering,Tokyo,Japan,135-8548","institution_ids":["https://openalex.org/I171481255"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067787810","display_name":"Takehiro Sato","orcid":"https://orcid.org/0000-0002-6253-0942"},"institutions":[{"id":"https://openalex.org/I22299242","display_name":"Kyoto University","ror":"https://ror.org/02kpeqv85","country_code":"JP","type":"education","lineage":["https://openalex.org/I22299242"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Takehiro Sato","raw_affiliation_strings":["Kyoto University Yoshida-Honmachi,Graduate School of Informatics,Sakyo-ku, Kyoto,Japan,606-8501"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Kyoto University Yoshida-Honmachi,Graduate School of Informatics,Sakyo-ku, Kyoto,Japan,606-8501","institution_ids":["https://openalex.org/I22299242"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5047825950","display_name":"Eiji Oki","orcid":"https://orcid.org/0000-0003-2177-5027"},"institutions":[{"id":"https://openalex.org/I22299242","display_name":"Kyoto University","ror":"https://ror.org/02kpeqv85","country_code":"JP","type":"education","lineage":["https://openalex.org/I22299242"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Eiji Oki","raw_affiliation_strings":["Kyoto University Yoshida-Honmachi,Graduate School of Informatics,Sakyo-ku, Kyoto,Japan,606-8501"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Kyoto University Yoshida-Honmachi,Graduate School of Informatics,Sakyo-ku, Kyoto,Japan,606-8501","institution_ids":["https://openalex.org/I22299242"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0074676,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"667","last_page":"670"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11704","display_name":"Mobile Crowdsensing and Crowdsourcing","score":0.9975000023841858,"subfield":{"id":"https://openalex.org/subfields/1706","display_name":"Computer Science Applications"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11704","display_name":"Mobile Crowdsensing and Crowdsourcing","score":0.9975000023841858,"subfield":{"id":"https://openalex.org/subfields/1706","display_name":"Computer Science Applications"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10326","display_name":"Indoor and Outdoor Localization Technologies","score":0.9973000288009644,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9811000227928162,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/crowdsensing","display_name":"Crowdsensing","score":0.8694734573364258},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8306390643119812},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.8172902464866638},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5960912704467773},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5391995906829834},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.45788446068763733},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4446144700050354},{"id":"https://openalex.org/keywords/mobile-device","display_name":"Mobile device","score":0.42178696393966675},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.13157019019126892}],"concepts":[{"id":"https://openalex.org/C2780821482","wikidata":"https://www.wikidata.org/wiki/Q25381721","display_name":"Crowdsensing","level":2,"score":0.8694734573364258},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8306390643119812},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.8172902464866638},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5960912704467773},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5391995906829834},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.45788446068763733},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4446144700050354},{"id":"https://openalex.org/C186967261","wikidata":"https://www.wikidata.org/wiki/Q5082128","display_name":"Mobile device","level":2,"score":0.42178696393966675},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.13157019019126892},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ccnc49033.2022.9700511","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ccnc49033.2022.9700511","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE 19th Annual Consumer Communications & Networking Conference (CCNC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1816103075","display_name":"Causal Relationship Information Sensing Platform","funder_award_id":"23K21659","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"}],"funders":[{"id":"https://openalex.org/F4320334764","display_name":"Japan Society for the Promotion of Science","ror":"https://ror.org/00hhkn466"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":9,"referenced_works":["https://openalex.org/W1890688605","https://openalex.org/W2083844448","https://openalex.org/W2095649738","https://openalex.org/W2167101736","https://openalex.org/W2473808492","https://openalex.org/W2918090311","https://openalex.org/W2956308693","https://openalex.org/W2970090765","https://openalex.org/W6639683144"],"related_works":["https://openalex.org/W2563347706","https://openalex.org/W2608033733","https://openalex.org/W4287604253","https://openalex.org/W1894261276","https://openalex.org/W2295662171","https://openalex.org/W2168877220","https://openalex.org/W4381785531","https://openalex.org/W4360776667","https://openalex.org/W3098657080","https://openalex.org/W4232477044"],"abstract_inverted_index":{"Sparse":[0],"mobile":[1,39],"crowdsensing":[2,5,40],"is":[3],"a":[4,53,69,74],"paradigm":[6],"that":[7,71],"reduces":[8],"the":[9,45,55,58,84,87],"sensing":[10],"cost":[11],"while":[12],"ensuring":[13],"data":[14,18,23,60,81,88],"quality":[15],"by":[16,82],"collecting":[17,100],"sparsely":[19],"and":[20,94,96],"reconstructing":[21,80],"desired":[22],"using":[24],"inference":[25,33,93],"algorithms":[26],"including":[27],"machine":[28,75],"learning":[29,76],"algorithms.":[30],"However,":[31],"real-time":[32],"of":[34,47,50,57,86,91],"spatial":[35],"information":[36],"with":[37],"sparse":[38],"has":[41],"not":[42],"sufficiently":[43],"considered":[44],"change":[46],"temporal":[48],"characteristics":[49],"data.":[51,102],"As":[52],"result,":[54],"accuracy":[56],"reconstructed":[59],"can":[61],"deteriorate":[62],"over":[63],"time.":[64],"Therefore,":[65],"this":[66],"paper":[67],"proposes":[68],"framework":[70],"periodically":[72],"updates":[73],"model":[77],"used":[78],"for":[79],"evaluating":[83],"importance":[85],"in":[89],"terms":[90],"both":[92],"re-training":[95],"giving":[97],"priority":[98],"to":[99],"important":[101]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}